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Friday 17th July, 09:00 - 10:30 Room: HT-102

Sample composition in online studies
Convenor	Mr Ulrich Krieger (German Internet Panel, SFB 884, University of Mannheim )

Session Details

This session focusses on sample composition in internet based research.

As not every member of the population has access to the web, online studies are prone to coverage error and thus resulting in a selective sample. This is problematic when researchers want to draw inference on the population as a whole, online and offline sample members.

Papers in this session explore the effect on sample composition when this mode is being used. Also measures to counter the effects of data collection via the web are being discussed.

Paper Details

1. What is the gain in a probability-based online panel to provide Internet access to sampling units that did not have access before?
Dr Melanie Revilla (RECSM - Universitat Pomepu Fabra)
Miss Anne Cornilleau (Centre de données sociopolitiques (Sciences Po / CNRS))
Dr Anne-sophie Cousteaux (Centre de données sociopolitiques (Sciences Po / CNRS))
Mr Stéphane Legleye (National Institute for Demographic Studies (INED); Inserm, U669; University Paris-Sud and University Paris Descartes)
Mr Pablo De Pedraza (Amsterdam Institute for Advanced labour Studies, University of Amsterdam, AIAS; Applied Economics Department, University of Salamanca)

We investigate what is the gain in terms of representativeness of proposing the equipment to non-Internet units in a web panel using tablets: the ELIPSS panel. We find that the number of non-Internet units that accept to participate is low, because of lower response rates but also due to the limited proportion of non-Internet units in the population. They also participate less in the specific surveys once they are panellists. However, they are very different from the Internet units. Therefore, there are a few important variables on which including them allows obtaining a more representative sample of

2. Parallel Phone and Web-based Interviews: Comparability and Validity.
Mr Randall Thomas (GfK Custom Research)
Mr David Krane (Nielsen Company)
Dr Frances Barlas (GfK Custom Research)

In a series of 4 studies, we compared attitudinal measures using opt-in samples with responses from probability samples. In addition, we compared a series of attitudinal measures of presidential approval over 10 years using opt-in samples with aggregate values obtained from probability samples. In all cases, we found significant convergence of results obtained from opt-in non-probability samples with those obtained from probability samples. We discuss both the similarities and differences in results.

3. Representative web-survey!
Mr Peter Linde (Statistics Denmark)

Web panels does not usually fulfil the basic demands with regard to representativeness. A simple random sample in a web-panel, e.g. advance stratified, would therefore, even if all participants provide a reply, not be representative, which is usually true of samples selected with a known probability in the overall population. But how close can you get? The discussion paper shows an example from Statistics Denmark, where representative sample surveys have asked for e-mail with regard to contact for new surveys. Proportional sampling by sex, age and geography are compared to proportional sample surveys surveys with more register

4. Measuring subjective well-being: do the use of web-surveys bias the results? Evidence from the 2013 GEM data from Luxembourg.
Mr Francesco Sarracino (Statistical Office of Luxembourg and Higher school of Economics)
Ms Malgorzata Mikucka (Universite Catholique de Louvain and Higher School of Economics)
Mr Cesare Riillo (Statistical Office of Luxembourg)

The aim of this work is to test whether web-surveys are a reliable tool to collect information about subjective well-being. The Global Entrepreneurship Monitor (GEM) data of 2013 provides information from a sample of 2000 people representative of the Luxembourgian population. Half of the sample answered to telephone interviews, whereas the remaining half used a web-survey. To test whether the use of web-surveys alters people’s self-assessment of their well-being, we use regression, decomposition analysis and propensity score matching. We identify a downward bias of
about 3.38%.